Relevant bibliographies by topics / Adaptive sensing

Academic literature on the topic 'Adaptive sensing'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Adaptive sensing"

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Davenport, Mark A., Andrew K. Massimino, Deanna Needell, and Tina Woolf. "Constrained Adaptive Sensing." IEEE Transactions on Signal Processing 64, no. 20 (October 15, 2016): 5437–49. http://dx.doi.org/10.1109/tsp.2016.2597130.

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Feng, Yan, and Xiaodong Wang. "Adaptive Multiband Spectrum Sensing." IEEE Wireless Communications Letters 1, no. 2 (April 2012): 121–24. http://dx.doi.org/10.1109/wcl.2012.022012.110230.

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Sun, Fang, Dongyue Xiao, Wei He, and Ran Li. "Adaptive Image Compressive Sensing Using Texture Contrast." International Journal of Digital Multimedia Broadcasting 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3902543.

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The traditional image Compressive Sensing (CS) conducts block-wise sampling with the same sampling rate. However, some blocking artifacts often occur due to the varying block sparsity, leading to a low rate-distortion performance. To suppress these blocking artifacts, we propose to adaptively sample each block according to texture features in this paper. With the maximum gradient in 8-connected region of each pixel, we measure the texture variation of each pixel and then compute the texture contrast of each block. According to the distribution of texture contrast, we adaptively set the sampling rate of each block and finally build an image reconstruction model using these block texture contrasts. Experimental results show that our adaptive sampling scheme improves the rate-distortion performance of image CS compared with the existing adaptive schemes and the reconstructed images by our method achieve better visual quality.
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Zhang, Xiaohua, Jiawei Chen, Hongyun Meng, and Xiaolin Tian. "Self-adaptive structured image sensing." Optical Engineering 51, no. 12 (December 4, 2012): 127001. http://dx.doi.org/10.1117/1.oe.51.12.127001.

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Malloy, Matthew L., and Robert D. Nowak. "Near-Optimal Adaptive Compressed Sensing." IEEE Transactions on Information Theory 60, no. 7 (July 2014): 4001–12. http://dx.doi.org/10.1109/tit.2014.2321552.

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Chen, Zichong, Juri Ranieri, Runwei Zhang, and Martin Vetterli. "DASS: Distributed Adaptive Sparse Sensing." IEEE Transactions on Wireless Communications 14, no. 5 (May 2015): 2571–83. http://dx.doi.org/10.1109/twc.2014.2388232.

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He, Lihan, Shihao Ji, Waymond R. Scott, and Lawrence Carin. "Adaptive Multimodality Sensing of Landmines." IEEE Transactions on Geoscience and Remote Sensing 45, no. 6 (June 2007): 1756–74. http://dx.doi.org/10.1109/tgrs.2007.894933.

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Li, Ran, Xiaomeng Duan, and Yongfeng Lv. "Adaptive compressive sensing of images using error between blocks." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878175. http://dx.doi.org/10.1177/1550147718781751.

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Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.
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JIANG, Chunxiao, Hongyang CHEN, Peisen ZHAO, Nengqiang HE, Canfeng CHEN, and Yong REN. "Adaptive Channel Sensing for Asynchronous Cooperative Spectrum Sensing Scheme." IEICE Transactions on Communications E96.B, no. 3 (2013): 918–22. http://dx.doi.org/10.1587/transcom.e96.b.918.

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Balasuriya, Arjuna, Henrik Schmidt, and Michael B. Benjamin. "Nested Autonomy ‐ Adaptive and collaborative sensing with hybrid sensing networks." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3905. http://dx.doi.org/10.1121/1.2935891.

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Dissertations / Theses on the topic "Adaptive sensing"

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Gonos, Theophile. "Bio-inspired adaptive sensing." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6217.

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Sensor array calibration is a major problem in engineering, to which a biological approach may provide alternative solutions. For animals, perception is relative. The aim of this thesis is to show that the relativity of perception in the animal kingdom could also be applied to robotics with promising results. This thesis explores through various behaviours and environments the properties of homeostatic mechanisms in sensory cells. It shows not only that the phenomenon can solve partial failure of sensors but also that it can be used by robots to adapt to their (changing) environment. Moreover the system shows emergent properties as well as adaptation to the robot body or its behaviour. The homeostatic mechanisms in biological neurons maintain fi ring activity between predefi ned ranges. Our model is designed to correct out of range neuron activity over a relatively long period of time (seconds or minutes). The system is implemented in a robot’s sensory neurons and is the only form of adaptability used in the central network. The robot was fi rst tested extensively with a mechanism implemented for obstacle avoidance and wall following behaviours. The robot was not only able to deal with sensor manufacture defects, but to adapt to changing environments (e.g. adapting to a narrow environment when it was originally in an open world). Emergence of non-implemented behaviours has also been observed. For example, during wall following behaviour, the robot seemed, at some point, bored. It changed the direction it was following the wall. Or we also noticed during obstacle avoidance an emerging exploratory behaviour. The model has also been tested on more complex behaviours such as skototaxis, an escape response, and phonotaxis. Again, especially with skototaxis, emergent behaviours appeared such as unpredictability on where and when the robot will be hiding. It appears that the adaptation is not only driven by the environment but by the behaviour of the robot too. It is by the complex feedback between these two things that non-implemented behaviours emerge. We showed that homeostasis can be used to improve sensory signal processing in robotics and we also found evidence that the phenomenon can be a necessary step towards better behavioural adaptation to the environment.
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Hyttinen, Emil. "Adaptive Grasping Using Tactile Sensing." Licentiate thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206395.

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Grasping novel objects is challenging because of incomplete object data and because of uncertainties inherent in real world applications. To robustly perform grasps on previously unseen objects, feedback from touch is essential. In our research, we study how information from touch sensors can be used to improve grasping novel objects. Since it is not trivial to extract relevant object properties and deduce appropriate actions from touch sensing, we employ machine learning techniques to learn suitable behaviors. We have shown that grasp stability estimation based on touch can be improved by including an approximate notion of object shape. Further we have devised a method to guide local grasp adaptations based on our stability estimation method. Grasp corrections are found by simulating tactile data for grasps in the vicinity of the current grasp. We present several experiments to demonstrate the applicability of our methods. The thesis is concluded by discussing our results and suggesting potential topics for further research.
Att greppa nya föremål är utmanande, både eftersom roboten inte har fullständig information om objekten och på grund av den inneboende osäkerheten i verkliga tillämpningar. Återkoppling från känselsensorer är viktigt för att kunna greppa föremål som inte påträffats tidigare. I vår forskning så studerar vi hur information från känselsensorer kan användas för att förbättra greppandet av nya föremål. Eftersom det är svårt att extrahera relevanta egenskaper om föremål och härleda lämpliga åtgärder, baserat på känselsensorer, så har vi använt maskininlärning för att lära roboten lämpliga beteenden. Vi har visat att uppskattningar av stabiliteten av ett grepp baserat på känselsensorer kan förbättras genom att även använda en grov approximation av föremålets form. Vi har även konstruerat en metod som vägleder lokala justeringar av grepp, baserat på vår metod som uppskattar stabiliteten av ett grepp. Dess justeringar hittas genom att simulera känselsensordata för grepp i närheten av det nuvarande greppet. Vi presenterar flera experiment som demonstrerar tillämpbarheten av våra metoder. Avhandlingen avslutas med en diskussion om våra resultat och förslag på möjliga ämnen för fortsatt forskning.

QC 20170510

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Rachuri, Kiran Kumar. "Smartphones based social sensing : adaptive sampling, sensing and computation offloading." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648104.

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Vakili, Arash. "Adaptive spectrum sensing for cognitive radio networks." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106425.

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Spectrum sensing is an important functionality of cognitive radio as a means to detect the presence or absence of the primary user (PU) in a certain spectrum band. Energy detection is a widely used spectrum sensing technique based on the assumption that the PU is either present or absent during the whole sensing period. However, this assumption is not realistic in a dynamic environment where the PU could appear or disappear at any time. The performance of the conventional energy detector (ED) actually deteriorates in the scenario where the PU activity status changes during the sensing period.Therefore, it is crucial to design a detector which can adapt to such an environment and reliably detect a change in the PU activity. Several sequential change detection techniques already exist in the literature; however, change detection in a fixed sensing duration has not been given enough attention. In this dissertation, three adaptive EDs are proposed to improve the detection performance in dynamic environments, where there is a single change in the PU activity during a fixed sensing period. In particular, we address the change detection problem using an exponential weighting approach and two theoretical approaches based on the composite hypothesis testing. In the first case, an intuitive idea of exponential weighting of the received energies is applied to design an adaptive ED that aims to satisfy the Neyman-Pearson (NP) criterion. The performance analysis and simulation results prove that the proposed adaptive ED outperforms the conventional ED and also the only existing adaptive ED in the literature that deals with the aforementioned issue. In the second case, two theoretical approaches based on the composite hypothesis testing are used to design two additional adaptive EDs that improve the change detection during the sensing period. The first approach, known as the generalized likelihood ratio test (GLRT), uses the maximum likelihood estimation (MLE) of the unknown change location in a likelihood ratio test. In this case, an iterative method is proposed to reduce the computational complexity of the MLE process. The second approach, referred to as composite-Bayesian, assumes that the unknown change location is a discrete random variable whose probability mass function (PMF) is available. The PU channel access pattern is modelled as a two-state Markov chain to obtain the PMF of the change location and the probability of occurrence of the two hypotheses. The resultant adaptive ED based on the GLRT approach aims to satisfy the NP criterion while the adaptive ED based on the composite-Bayesian approach aims to minimize the probability of error. It is demonstrated through simulations that these two proposed adaptive EDs have superior performance over the conventional ED. Furthermore, the GLRT-based adaptive ED outperforms the first proposed adaptive ED based on the exponential weighting approach.
La détection de spectre est une fonctionnalité importante de la radio cognitive car elle permet de vérifier la présence ou l'absence d'un utilisateur principal (PU) sur une bande de spectre donnée. La détection de l'énergie est une méthode fréquemment utilisée pour y parvenir.Cette dernière s'appuie sur l'hypothèse que le PU est présent ou absent pour la totalité de la période de mesure. Cependant, cette hypothèse n'est pas réaliste pour un environnement dynamique dans lequel le PU peut apparaître ou disparaître à n'importe quel instant. En effet, les performances d'un détecteur d'énergie conventionnel (ED) se détériorent lorsque l'état du PU varie au cours de la période durant laquelle les mesures sont effectuées. C'est donc pour cette raison qu'il est nécessaire de concevoir un détecteurqui s'adapte bien à ce genre d'environnement et qui permet de détecter de manière fiable tout changement dans l'activité du PU. Plusieurs techniques de détection de changements séquentiels existent dans la littérature mais la détection de changement pour une durée fixe n'a pas été explorée suffisamment en détails. Dans le cadre de ce mémoire, trois EDs adaptatifs sont proposés dans le but d'améliorer les performances dans un environnement dynamique au sein duquel il y a un seul changement au niveau de l'activité du PU et ce durant une période de mesure de durée fixe. Pour tenter de résoudre cette problématique, une approche à pondération exponentielle et deux approches théoriques en lien avec le test d'hypothèse composée sont proposées. Dans le premier cas, une approche intuitive exploitant la pondération exponentielle de l'énergie mesurée est utilisée afin de concevoir un ED adaptatif qui satisfait le critère de Neyman-Pearson (NP). L'analyse des performances et des résultats de simulation prouvent que cette stratégie offre de meilleures performances par rapport aux ED conventionnels. Il s'agit également du seul ED adaptatif présent dans la littérature qui tente de résoudre la problématique précédemment mentionnée. Dans le second cas, deux approches théoriques fondées sur le test d'hypothèse composée sont utilisées afin de concevoir deux nouveaux EDs adaptatifs qui améliorent la détection de changements durant la période de mesure. La première approche s'appuie sur le test généralisé de vraisemblance (GLRT) et utilise une estimation de la vraisemblance maximale (MLE) de la position inconnue du changement. Dans ce cas, une méthode itérative est proposée pour réduire la complexité de calcul du processus de MLE. La deuxième approche, dite composée bayésienne, prend pour acquis que la position inconnue du changement est une variable aléatoire discrète dont la loi de probabilité (PMF) est connue. Pour cette dernière approche, les accès au canal sont modélisés par un modèle de Markov à deux états afin d'obtenir la PMF de la position du changement et la probabilité d'occurrence des deux hypothèses. Le ED adaptatif utilisant le GLRT tente de satisfaire le critère de NP tandis que le ED adaptatif utilisant l'approche de la composée bayésienne tente de minimiser la probabilité d'une erreur. Il est démontré à l'aide de simulations que ces deux EDs adaptatifs offrent des performances supérieures à celles du ED conventionnel. En outre, le ED adaptatif utilisant le GLRT surpasse le ED adaptive utilisant l'approche pondération exponentielle.
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Oag, Thomas J. D. "Interferometric wavefront sensing for extreme adaptive optics." Thesis, Durham University, 2004. http://etheses.dur.ac.uk/3101/.

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Adaptive optics is concerned with the correction of phase distortions in wavefronts which degrade the quality of images produced by optical systems. It was originally developed for both astronomy and the military, where the Earth's atmosphere causes distortions, although other uses are now being developed. As ground based telescopes become increasingly large the size and complexity of adaptive optics systems also increase, creating "extreme adaptive optics”. This thesis deals with such an adaptive optics system. A novel self referenced phase shifting interferometer based on a liquid crystal (LC) waveplate is presented which can measure high spatial frequency phase distortions. This is then coupled to a LC spatial light modulator wavefront corrector. The geometry is matched such that there is no need for a wavefront reconstructor. The performance is measured in two stages. Firstly, spatially where static phase distortions are measured by the interferometer and corrected. Secondly, temporally where a simple analogue feedback is implemented to show correction over a single corrector pixel for fast time varying phase distortions. This work builds on other published research on using point diffraction interferometry in adaptive optics. The novelty lies in the development of a new implementation of a point diffraction interferometer, and in the demonstration of a high-speed closed loop single channel system. This work therefore contributes to the groundwork required to build an extreme adaptive optics system whose complexity scales linearly with the size (area) of the telescope aperture.
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Ash, Darren. "Optimal wavefront sensing for adaptive optics systems." Thesis, University of Kent, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297368.

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Roddier, Nicolas 1965. "Curvature sensing for Adaptive Optics: A computer simulation." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/291948.

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This thesis describes computer simulations of a new wavefront sensing technique for Adaptive Optics based on local wavefront curvature measurements, along with edge slope measurements. The output signal from the curvature measurements, along with edge slope measurements. The output signal from the curvature sensor can be directly applied to the electrodes of a bimorph or membrane mirror. The mirror is used as an analog device to solve the Poisson Equation, providing a fast real time compensation for atmospheric disturbances. The open loop characteristics of the system are presented. The ideal response is analyzed, and side effects such as non-linearity, photon and diffraction noises are discussed. Closed loop simulations are presented thereafter. A seven actuator system showed a few unstable modes. A 13 actuator system with proper filtering corrects the atmospheric perturbations. To simulate atmospheric distorted wavefronts, an algorithm based on spectral decomposition of the Zernike covariance matrix was derived. This sensor can also be used to test large telescope mirrors using a modified program that solves the Poisson Equation with Neumann boundary conditions.
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van, Dam Marcos Alejandro. "Wave-front sensing for adaptive optics in astronomy." Thesis, University of Canterbury. Electrical and Computer Engineering, 2002. http://hdl.handle.net/10092/1149.

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Optical images of astronomical objects viewed through ground-based telescopes are blurred by the atmosphere. The atmosphere is turbulent and as a consequence the density of air is not evenly distributed. This results in random, time-varying variations in refractive index. The wave-fronts passing through the atmosphere become aberrated, degrading the quality of the images. One solution is to include an adaptive optics system in the telescope. The system estimates the aberration of the wave-fronts and compensates the wave-front in real time using a corrector element, typically a deformable mirror. An important problem is how to estimate the aberrations optimally using only a small amount of light. This procedure is called wave-front sensing and is the subject of the research of this thesis. For turbulence with Kolmogorov statistics, the wave-front slope contains 87% of the energy of the aberrations. Hence, it is crucial to estimate the slope accurately. The displacement of an image is directly proportional to the wave-front slope and is used to estimate the slope. The conventional way of measuring the average slope of the wave-front in a Shack-Hartmann sensor is from the centroid of the image at the focal plane. It is demonstrated that using the centroid estimator produces an estimate with infinite variance. The Cramer-Rao lower bound (CRLB) is a theoretical lower bound for the variance of an unbiased estimator. The variance of the maximum-likelihood (ML) estimate for the displacement of a diffraction-limited image approaches the CRLB using a relatively small number of photons. The ML estimator is extended to the case where the image is randomly blurred by atmospheric turbulence. It is found that the variance of the error of the slope estimator can be improved significantly at low turbulence levels by using the ML estimator instead of the centroid. Curvature sensors use two defocused images to estimate the wave-front aberrations. It is shown using the CRLB that the focal plane is the optimal plane to measure the slope and the error using defocused images is quantified. The effect of using broadband light on the accuracy of the slope estimate is also investigated. When using laser guide stars, it is not possible to estimate the slope of the wave-front directly from the image because the beam is displaced on both the upward and downward journey. However, the displacement is a weak function of wavelength due to dispersion. In theory, the difference in wave-front slope as a function of wavelength is proportional to the absolute slope. Centering algorithms were implemented on experimental data taken at the Observatoire de Lyon to confirm this relationship. There is strong evidence pointing to a linear relationship between two pairs of differential tilt measurements, but not between the differential and the absolute tilt. However, the data appears to have been affected by a systematic experimental error and a new experiment is needed. Phase retrieval is a non-linear technique used to recover the phase in the Fourier domain using intensity measurements at the image plane and additional constraints. A method is described to solve the phase retrieval problem using linear iterations near the solution, which provides both analytical insight into phase retrieval and numerical results. The algorithm finds the maximum a posteriori estimate of the phase using prior information about the statistics of the noise and the phase and converges well in practice. When phase retrieval is performed on data from subdivided apertures, there is a loss of information regarding the relative piston terms of the subapertures and this error is quantified. It is found that there is a smaller wavefront error when estimating the phase from a full aperture than from a subdivided aperture. Using a combination of intensity measurements from a full and a subdivided aperture is shown to result in a small improvement at very high photon levels only. Curvature sensors measure the wave-front aberrations via a linear relationship between the curvature of the wave-front and the intensity difference between two defocused images. In practice, their performance is limited by their non-linear behaviour, which is characterised by solving simultaneously the irradiance transport equation and the accompanying wave-front transport equation. It is shown how the presence of non-linear geometric terms limits the accuracy of the sensor and how diffraction effects limit the spatial resolution. The effect of photon noise on the sensor is also quantified. A novel technique for deriving wave-front aberrations from two defocused intensity measurements is derived. The intensity defines a probability density function and the method is based on the evolution of the cumulative density function of the intensity. In one dimension, the problem is easily solved using histogram specification with a linear relationship between the wave-front slope and the difference in the abscissas of the histograms. This method is insensitive to scintillation. In two dimensions, the procedure requires the use of the Radon transform. Simulation results demonstrate that very good reconstructions can be attained down to 100 photons in each detector.
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Al, Marzouqi Hasan. "Curvelet transform with adaptive tiling." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52961.

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In this dissertation we address the problem of adapting frequency domain tiling using the curvelet transform as the basis algorithm. The optimal tiling, for a given class of images, is computed using denoising performance as the cost function. The major adaptations considered are: the number of scale decompositions, angular decompositions per scale/quadrant, and scale locations. A global optimization algorithm combining the three adaptations is proposed. Denoising performance of adaptive curvelets is tested on seismic and face data sets. The developed adaptation procedure is applied to a number of different application areas. Adaptive curvelets are used to solve the problem of sparse data recovery from subsampled measurements. Performance comparison with default curvelets demonstrates the effectiveness of the adaptation scheme. Adaptive curvelets are also used in the development of a novel image similarity index. The developed measure succeeds in retrieving correct matches from a variety of textured materials. Furthermore, we present an algorithm for classifying different types of seismic activities.
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Ruggiu, Jean-Marc. "Optimal control for adaptive optics." Thesis, University of Kent, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342157.

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Books on the topic "Adaptive sensing"

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Dongen, Joost T. van, and Francesco Licausi. Low-oxygen stress in plants: Oxygen sensing and adaptive responses to hypoxia. Wien: Springer Verlag, 2013.

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Driels, Morris. Adaptive control of direct drive dexterous robotic hand with bilateral tactile sensing. Monterey, Calif: Naval Postgraduate School, 1990.

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(Society), SPIE, and SPIE Europe, eds. Optics in atmospheric propagation and adaptive systems XII: 1-3 September 2009, Berlin, Germany. Bellingham, Wash: SPIE, 2009.

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Stein, Karin. Optics in atmospheric propagation and adaptive systems XIII: 20-21 September 2010, Toulouse, France. Edited by SPIE (Society) and European Association of Remote Sensing Companies. Bellingham, Wash: SPIE, 2010.

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Stein, Karin. Optics in atmospheric propagation and adaptive systems XIII: 20-21 September 2010, Toulouse, France. Edited by SPIE (Society) and European Association of Remote Sensing Companies. Bellingham, Wash: SPIE, 2010.

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P, Lukin V., Society of Photo-optical Instrumentation Engineers., and Society of Photo-optical Instrumentation Engineers. Russian Chapter., eds. Wave propagation in the atmosphere and adaptive optics: Selected research papers on wave propagation in the atmosphere and adaptive optics 2000. Bellingham, Wash., USA: SPIE, 2000.

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(Editor), Christian Werner, Adam D. Devir (Editor), and Anton Kohnle (Editor), eds. Optics in Atmospheric Propagation, Adaptive Systems, and Lidar Techniques for Remote Sensing: 24-26 September 1996, Taormina, Italy (Optics in Atmospheric Propagation, Adaptive Systems, & Lidat). SPIE-International Society for Optical Engine, 1997.

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Koichi, Iwata, Nihon Kōgakkai (Ōyō Butsuri Gakkai), Society of Photo-optical Instrumentation Engineers., and Denki Gakkai (1888), eds. Optical engineering for sensing and nanotechnology (ICOSN 2001): 6-8 June, 2001, Yokohama, Japan. Bellingham, Wash., USA: SPIE, 2001.

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Anton, Kohnle, Society of Photo-optical Instrumentation Engineers., and Centre national d'études spatiales (France), eds. Optics in atmospheric propagation and adaptive systems: 27-28 September 1995, Paris, France. Bellingham, Wash., USA: SPIE, 1995.

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D, Devir Adam, Consiglio nazionale delle ricerche (Italy), and Conference on Optics in Atmospheric Propagation and Adaptive Systems (4th : 1998 : Barcelona, Spain), eds. Atmospheric propagation, adaptive systems, and lidar techniques for remote sensing II: 23-24 September 1998, Barcelona, Spain. Bellingham, Wash: SPIE, 1998.

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Book chapters on the topic "Adaptive sensing"

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Rousset, G. "Wavefront Sensing." In Adaptive Optics for Astronomy, 115–38. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-015-8265-0_7.

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Lau, C. P. "Activity-Sensing Rate-Adaptive Pacing." In Rate Adaptive Cardiac Pacing, 83–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-76649-7_7.

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Wolpert, David, and Paul Ampadu. "Sensing Temperature Dependence." In Managing Temperature Effects in Nanoscale Adaptive Systems, 35–61. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0748-5_3.

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Yoon, Geunyoung. "Wavefront Sensing and Diagnostic Uses." In Adaptive Optics for Vision Science, 63–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471914878.ch3.

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Kurtz, Rafael. "Adaptive encoding of motion information in the fly visual system." In Frontiers in Sensing, 115–28. Vienna: Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-211-99749-9_8.

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TAN, Ruilian. "Research on Adaptive Cooperative Spectrum Sensing." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 487–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49109-7_46.

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Zatorre, Guillermo, Nicolás Medrano, Santiago Celma, Bonifacio Martín-del-Brío, and Antonio Bono. "Smart Sensing with Adaptive Analog Circuits." In Computational Intelligence and Bioinspired Systems, 463–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_57.

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Qiu, Zhijin, Naijun Hu, Zhongwen Guo, Like Qiu, Shuai Guo, and Xi Wang. "IoT Sensing Parameters Adaptive Matching Algorithm." In Big Data Computing and Communications, 198–211. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42553-5_17.

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Huang, Zhenhua, Fangxu Dong, and Arthur C. Sanderson. "Adaptive Distributed Sensing and Control Methods." In Handbook of Advanced Lighting Technology, 1–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-00295-8_30-1.

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Huang, Zhenhua, Fangxu Dong, and Arthur C. Sanderson. "Adaptive Distributed Sensing and Control Methods." In Handbook of Advanced Lighting Technology, 535–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-00176-0_30.

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Conference papers on the topic "Adaptive sensing"

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Muller, Ralf R., Ali Bereyhi, and Christoph Mecklcnbraukcr. "Oversampled Adaptive Sensing." In 2018 Information Theory and Applications Workshop (ITA). IEEE, 2018. http://dx.doi.org/10.1109/ita.2018.8503191.

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Zhang, Wenyi, Ahmed K. Sadek, Cong Shen, and Stephen J. Shellhammer. "Adaptive spectrum sensing." In 2010 Information Theory and Applications Workshop (ITA). IEEE, 2010. http://dx.doi.org/10.1109/ita.2010.5454086.

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Luttrell, Stephen P. "Adaptive Bayesian networks." In Aerospace Sensing, edited by Firooz A. Sadjadi. SPIE, 1992. http://dx.doi.org/10.1117/12.139939.

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Rousset, Gerard. "Wavefront Sensing for AO." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/aopt.2007.atub1.

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Vievard, Sébastien, Steven P. Bos, Frédéric Cassaing, Thayne Currie, Vincent Deo, Olivier Guyon, Nemanja Jovanovic, et al. "Focal plane wavefront sensing on SUBARU/SCExAO." In Adaptive Optics Systems VII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2020. http://dx.doi.org/10.1117/12.2562787.

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Vohnsen, Brian, and Denise Valente. "Sensing Wavefront Slopes using Intensity Gradients." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/aoms.2016.aow1b.4.

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Gao, Fei, Xiaohua Feng, Ruochong Zhang, Siyu Liu, and Yuanjin Zheng. "Adaptive coherent photoacoustic sensing." In Photons Plus Ultrasound: Imaging and Sensing 2018, edited by Alexander A. Oraevsky and Lihong V. Wang. SPIE, 2018. http://dx.doi.org/10.1117/12.2292627.

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Haupt, Jarvis. "Session TA8b3: Adaptive sensing." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190212.

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Barkan, Oren, Jonathan Weill, Amir Averbuch, and Shai Dekel. "Adaptive Compressed Tomography Sensing." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.285.

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Hsu, Charles, Ming K. Hsu, Jae Cha, Tomo Iwamura, Joseph Landa, Charles Nguyen, and Harold Szu. "Adaptive compressive sensing camera." In SPIE Defense, Security, and Sensing, edited by Harold H. Szu. SPIE, 2013. http://dx.doi.org/10.1117/12.2017881.

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Reports on the topic "Adaptive sensing"

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Barrett, Terence W. Adaptive RF Sensing. Fort Belvoir, VA: Defense Technical Information Center, June 1999. http://dx.doi.org/10.21236/ada369898.

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Schmidt, Henrik, John J. Leonard, and David Battle. GOATS 2005: Integrated, Adaptive Autonomous Acoustic Sensing Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada611929.

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Schmidt, Henrik, John J. Leonard, and David Battle. GOATS 2005 Integrated, Adaptive Autonomous Acoustic Sensing Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada569082.

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Zimet, Lior, Morteza Shahram, and Peyman Milanfar. An Adaptive Framework for Image and Video Sensing. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada460915.

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Levchuk, Georgiy, Andres Ortiz, and John-Collonna Romano. Distributed Sensing and Processing Adaptive Collaboration Environment (D-SPACE). Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada608436.

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Shelnutt, John Allen, Frank B. van Swol, Zhongchun Wang, and Craig J. Medforth. LDRD final report on adaptive-responsive nanostructures for sensing applications. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/875634.

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Carin, Lawrence. Adaptive Sensing and Fusion of Multi-Sensor Data and Historical Information. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada509471.

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Driels, Morris R. Adaptive Control of Direct Drive Dexterous Robotic Hand with Bilateral Tactile Sensing. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada233980.

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Roggemann, Michael C., and Timothy J. Schulz. Image Reconstruction, Wave Front Sensing, and Adaptive Optics in Extreme Atmospheric Seeing Conditions. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada484211.

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Zhang, Junshan. Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada590144.

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